Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations100000
Missing cells124283
Missing cells (%)3.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.4 MiB
Average record size in memory256.0 B

Variable types

DateTime1
Categorical6
Numeric21
Text4

Alerts

AIRLINE is highly overall correlated with AIRLINE_CODE and 2 other fieldsHigh correlation
AIRLINE_CODE is highly overall correlated with AIRLINE and 2 other fieldsHigh correlation
AIRLINE_DOT is highly overall correlated with AIRLINE and 2 other fieldsHigh correlation
AIR_TIME is highly overall correlated with CANCELLED and 4 other fieldsHigh correlation
ARR_DELAY is highly overall correlated with CANCELLED and 2 other fieldsHigh correlation
ARR_TIME is highly overall correlated with CANCELLED and 5 other fieldsHigh correlation
CANCELLATION_CODE is highly overall correlated with CANCELLED and 6 other fieldsHigh correlation
CANCELLED is highly overall correlated with AIR_TIME and 6 other fieldsHigh correlation
CRS_ARR_TIME is highly overall correlated with ARR_TIME and 4 other fieldsHigh correlation
CRS_DEP_TIME is highly overall correlated with ARR_TIME and 4 other fieldsHigh correlation
CRS_ELAPSED_TIME is highly overall correlated with AIR_TIME and 2 other fieldsHigh correlation
DELAY_DUE_CARRIER is highly overall correlated with CANCELLATION_CODEHigh correlation
DELAY_DUE_LATE_AIRCRAFT is highly overall correlated with CANCELLATION_CODEHigh correlation
DELAY_DUE_NAS is highly overall correlated with CANCELLATION_CODEHigh correlation
DELAY_DUE_SECURITY is highly overall correlated with CANCELLATION_CODEHigh correlation
DELAY_DUE_WEATHER is highly overall correlated with CANCELLATION_CODEHigh correlation
DEP_DELAY is highly overall correlated with ARR_DELAYHigh correlation
DEP_TIME is highly overall correlated with ARR_TIME and 4 other fieldsHigh correlation
DISTANCE is highly overall correlated with AIR_TIME and 2 other fieldsHigh correlation
DIVERTED is highly overall correlated with AIR_TIME and 3 other fieldsHigh correlation
DOT_CODE is highly overall correlated with AIRLINE and 2 other fieldsHigh correlation
ELAPSED_TIME is highly overall correlated with AIR_TIME and 4 other fieldsHigh correlation
TAXI_IN is highly overall correlated with CANCELLEDHigh correlation
WHEELS_OFF is highly overall correlated with ARR_TIME and 4 other fieldsHigh correlation
WHEELS_ON is highly overall correlated with ARR_TIME and 5 other fieldsHigh correlation
CANCELLED is highly imbalanced (82.5%) Imbalance
DIVERTED is highly imbalanced (97.7%) Imbalance
DEP_TIME has 2576 (2.6%) missing values Missing
DEP_DELAY has 2577 (2.6%) missing values Missing
TAXI_OUT has 2618 (2.6%) missing values Missing
WHEELS_OFF has 2618 (2.6%) missing values Missing
WHEELS_ON has 2655 (2.7%) missing values Missing
TAXI_IN has 2655 (2.7%) missing values Missing
ARR_TIME has 2655 (2.7%) missing values Missing
ARR_DELAY has 2852 (2.9%) missing values Missing
CANCELLATION_CODE has 97373 (97.4%) missing values Missing
ELAPSED_TIME has 2852 (2.9%) missing values Missing
AIR_TIME has 2852 (2.9%) missing values Missing
DELAY_DUE_WEATHER is highly skewed (γ1 = 44.38001929) Skewed
DELAY_DUE_NAS is highly skewed (γ1 = 24.49245129) Skewed
DELAY_DUE_SECURITY is highly skewed (γ1 = 119.2410852) Skewed
DEP_DELAY has 4851 (4.9%) zeros Zeros
ARR_DELAY has 1853 (1.9%) zeros Zeros
DELAY_DUE_CARRIER has 90096 (90.1%) zeros Zeros
DELAY_DUE_WEATHER has 98965 (99.0%) zeros Zeros
DELAY_DUE_NAS has 91256 (91.3%) zeros Zeros
DELAY_DUE_SECURITY has 99898 (99.9%) zeros Zeros
DELAY_DUE_LATE_AIRCRAFT has 91377 (91.4%) zeros Zeros

Reproduction

Analysis started2025-07-07 15:08:23.003777
Analysis finished2025-07-07 15:09:44.031429
Duration1 minute and 21.03 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct1704
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
Minimum2019-01-01 00:00:00
Maximum2023-08-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-07T15:09:44.187130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:44.401273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

AIRLINE
Categorical

High correlation 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
Southwest Airlines Co.
19150 
Delta Air Lines Inc.
13070 
American Airlines Inc.
12874 
SkyWest Airlines Inc.
11306 
United Air Lines Inc.
8506 
Other values (13)
35094 

Length

Max length34
Median length22
Mean length19.59848
Min length9

Characters and Unicode

Total characters1959848
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAllegiant Air
2nd rowAmerican Airlines Inc.
3rd rowPSA Airlines Inc.
4th rowSouthwest Airlines Co.
5th rowSouthwest Airlines Co.

Common Values

ValueCountFrequency (%)
Southwest Airlines Co. 19150
19.1%
Delta Air Lines Inc. 13070
13.1%
American Airlines Inc. 12874
12.9%
SkyWest Airlines Inc. 11306
11.3%
United Air Lines Inc. 8506
8.5%
Republic Airline 4955
 
5.0%
Envoy Air 3958
 
4.0%
Endeavor Air Inc. 3748
 
3.7%
JetBlue Airways 3729
 
3.7%
PSA Airlines Inc. 3662
 
3.7%
Other values (8) 15042
15.0%

Length

2025-07-07T15:09:44.612516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc 61939
20.1%
airlines 56414
18.3%
air 34902
11.3%
lines 24787
8.1%
southwest 19150
 
6.2%
co 19150
 
6.2%
delta 13070
 
4.2%
american 12874
 
4.2%
skywest 11306
 
3.7%
united 8506
 
2.8%
Other values (18) 45725
14.9%

Most occurring characters

ValueCountFrequency (%)
i 225519
11.5%
207823
 
10.6%
n 182787
 
9.3%
e 174571
 
8.9%
r 125542
 
6.4%
s 122260
 
6.2%
A 121745
 
6.2%
l 90075
 
4.6%
t 82711
 
4.2%
. 81089
 
4.1%
Other values (33) 545726
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1959848
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 225519
11.5%
207823
 
10.6%
n 182787
 
9.3%
e 174571
 
8.9%
r 125542
 
6.4%
s 122260
 
6.2%
A 121745
 
6.2%
l 90075
 
4.6%
t 82711
 
4.2%
. 81089
 
4.1%
Other values (33) 545726
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1959848
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 225519
11.5%
207823
 
10.6%
n 182787
 
9.3%
e 174571
 
8.9%
r 125542
 
6.4%
s 122260
 
6.2%
A 121745
 
6.2%
l 90075
 
4.6%
t 82711
 
4.2%
. 81089
 
4.1%
Other values (33) 545726
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1959848
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 225519
11.5%
207823
 
10.6%
n 182787
 
9.3%
e 174571
 
8.9%
r 125542
 
6.4%
s 122260
 
6.2%
A 121745
 
6.2%
l 90075
 
4.6%
t 82711
 
4.2%
. 81089
 
4.1%
Other values (33) 545726
27.8%

AIRLINE_DOT
Categorical

High correlation 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
Southwest Airlines Co.: WN
19150 
Delta Air Lines Inc.: DL
13070 
American Airlines Inc.: AA
12874 
SkyWest Airlines Inc.: OO
11306 
United Air Lines Inc.: UA
8506 
Other values (13)
35094 

Length

Max length38
Median length26
Mean length23.59848
Min length13

Characters and Unicode

Total characters2359848
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAllegiant Air: G4
2nd rowAmerican Airlines Inc.: AA
3rd rowPSA Airlines Inc.: OH
4th rowSouthwest Airlines Co.: WN
5th rowSouthwest Airlines Co.: WN

Common Values

ValueCountFrequency (%)
Southwest Airlines Co.: WN 19150
19.1%
Delta Air Lines Inc.: DL 13070
13.1%
American Airlines Inc.: AA 12874
12.9%
SkyWest Airlines Inc.: OO 11306
11.3%
United Air Lines Inc.: UA 8506
8.5%
Republic Airline: YX 4955
 
5.0%
Envoy Air: MQ 3958
 
4.0%
Endeavor Air Inc.: 9E 3748
 
3.7%
JetBlue Airways: B6 3729
 
3.7%
PSA Airlines Inc.: OH 3662
 
3.7%
Other values (8) 15042
15.0%

Length

2025-07-07T15:09:44.798441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc 61939
15.2%
airlines 56414
13.8%
air 34902
 
8.6%
lines 24787
 
6.1%
southwest 19150
 
4.7%
co 19150
 
4.7%
wn 19150
 
4.7%
delta 13070
 
3.2%
dl 13070
 
3.2%
american 12874
 
3.2%
Other values (36) 133317
32.7%

Most occurring characters

ValueCountFrequency (%)
307823
 
13.0%
i 225519
 
9.6%
n 182787
 
7.7%
e 174571
 
7.4%
A 160465
 
6.8%
r 125542
 
5.3%
s 122260
 
5.2%
: 100000
 
4.2%
l 90075
 
3.8%
t 82711
 
3.5%
Other values (45) 788095
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2359848
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
307823
 
13.0%
i 225519
 
9.6%
n 182787
 
7.7%
e 174571
 
7.4%
A 160465
 
6.8%
r 125542
 
5.3%
s 122260
 
5.2%
: 100000
 
4.2%
l 90075
 
3.8%
t 82711
 
3.5%
Other values (45) 788095
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2359848
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
307823
 
13.0%
i 225519
 
9.6%
n 182787
 
7.7%
e 174571
 
7.4%
A 160465
 
6.8%
r 125542
 
5.3%
s 122260
 
5.2%
: 100000
 
4.2%
l 90075
 
3.8%
t 82711
 
3.5%
Other values (45) 788095
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2359848
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
307823
 
13.0%
i 225519
 
9.6%
n 182787
 
7.7%
e 174571
 
7.4%
A 160465
 
6.8%
r 125542
 
5.3%
s 122260
 
5.2%
: 100000
 
4.2%
l 90075
 
3.8%
t 82711
 
3.5%
Other values (45) 788095
33.4%

AIRLINE_CODE
Categorical

High correlation 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
WN
19150 
DL
13070 
AA
12874 
OO
11306 
UA
8506 
Other values (13)
35094 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters200000
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowG4
2nd rowAA
3rd rowOH
4th rowWN
5th rowWN

Common Values

ValueCountFrequency (%)
WN 19150
19.1%
DL 13070
13.1%
AA 12874
12.9%
OO 11306
11.3%
UA 8506
8.5%
YX 4955
 
5.0%
MQ 3958
 
4.0%
9E 3748
 
3.7%
B6 3729
 
3.7%
OH 3662
 
3.7%
Other values (8) 15042
15.0%

Length

2025-07-07T15:09:44.965051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 19150
19.1%
dl 13070
13.1%
aa 12874
12.9%
oo 11306
11.3%
ua 8506
8.5%
yx 4955
 
5.0%
mq 3958
 
4.0%
9e 3748
 
3.7%
b6 3729
 
3.7%
oh 3662
 
3.7%
Other values (8) 15042
15.0%

Most occurring characters

ValueCountFrequency (%)
A 38720
19.4%
O 26274
13.1%
N 22361
11.2%
W 19150
9.6%
D 13070
 
6.5%
L 13070
 
6.5%
U 8506
 
4.3%
Y 7065
 
3.5%
9 5945
 
3.0%
X 5621
 
2.8%
Other values (12) 40218
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 200000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 38720
19.4%
O 26274
13.1%
N 22361
11.2%
W 19150
9.6%
D 13070
 
6.5%
L 13070
 
6.5%
U 8506
 
4.3%
Y 7065
 
3.5%
9 5945
 
3.0%
X 5621
 
2.8%
Other values (12) 40218
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 200000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 38720
19.4%
O 26274
13.1%
N 22361
11.2%
W 19150
9.6%
D 13070
 
6.5%
L 13070
 
6.5%
U 8506
 
4.3%
Y 7065
 
3.5%
9 5945
 
3.0%
X 5621
 
2.8%
Other values (12) 40218
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 200000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 38720
19.4%
O 26274
13.1%
N 22361
11.2%
W 19150
9.6%
D 13070
 
6.5%
L 13070
 
6.5%
U 8506
 
4.3%
Y 7065
 
3.5%
9 5945
 
3.0%
X 5621
 
2.8%
Other values (12) 40218
20.1%

DOT_CODE
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19977.258
Minimum19393
Maximum20452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:45.129135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19393
5-th percentile19393
Q119790
median19930
Q320368
95-th percentile20436
Maximum20452
Range1059
Interquartile range (IQR)578

Descriptive statistics

Standard deviation377.22316
Coefficient of variation (CV)0.01888263
Kurtosis-1.30732
Mean19977.258
Median Absolute Deviation (MAD)374
Skewness-0.23188721
Sum1.9977258 × 109
Variance142297.31
MonotonicityNot monotonic
2025-07-07T15:09:45.324127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
19393 19150
19.1%
19790 13070
13.1%
19805 12874
12.9%
20304 11306
11.3%
19977 8506
8.5%
20452 4955
 
5.0%
20398 3958
 
4.0%
20363 3748
 
3.7%
20409 3729
 
3.7%
20397 3662
 
3.7%
Other values (8) 15042
15.0%
ValueCountFrequency (%)
19393 19150
19.1%
19687 666
 
0.7%
19690 1000
 
1.0%
19790 13070
13.1%
19805 12874
12.9%
19930 3466
 
3.5%
19977 8506
8.5%
20304 11306
11.3%
20363 3748
 
3.7%
20366 649
 
0.6%
ValueCountFrequency (%)
20452 4955
5.0%
20436 2197
2.2%
20416 3211
3.2%
20409 3729
3.7%
20398 3958
4.0%
20397 3662
3.7%
20378 2110
2.1%
20368 1743
 
1.7%
20366 649
 
0.6%
20363 3748
3.7%

FL_NUMBER
Real number (ℝ)

Distinct6541
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2511.9107
Minimum1
Maximum8819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:45.543295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile262
Q11054
median2150
Q33794
95-th percentile5651
Maximum8819
Range8818
Interquartile range (IQR)2740

Descriptive statistics

Standard deviation1745.6329
Coefficient of variation (CV)0.69494228
Kurtosis-0.89656803
Mean2511.9107
Median Absolute Deviation (MAD)1333
Skewness0.50518122
Sum2.5119107 × 108
Variance3047234.3
MonotonicityNot monotonic
2025-07-07T15:09:45.744896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
676 54
 
0.1%
320 53
 
0.1%
334 52
 
0.1%
350 48
 
< 0.1%
61 48
 
< 0.1%
710 47
 
< 0.1%
55 47
 
< 0.1%
517 46
 
< 0.1%
678 46
 
< 0.1%
777 45
 
< 0.1%
Other values (6531) 99514
99.5%
ValueCountFrequency (%)
1 30
< 0.1%
2 24
< 0.1%
3 30
< 0.1%
4 21
< 0.1%
5 28
< 0.1%
6 32
< 0.1%
7 21
< 0.1%
8 26
< 0.1%
9 14
< 0.1%
10 27
< 0.1%
ValueCountFrequency (%)
8819 1
< 0.1%
8795 1
< 0.1%
8783 1
< 0.1%
8771 1
< 0.1%
7439 1
< 0.1%
7438 2
< 0.1%
7436 1
< 0.1%
7434 2
< 0.1%
7429 2
< 0.1%
7426 1
< 0.1%

ORIGIN
Text

Distinct372
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:46.235237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300000
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowPGD
2nd rowDFW
3rd rowEWN
4th rowABQ
5th rowPIT
ValueCountFrequency (%)
atl 5099
 
5.1%
dfw 4444
 
4.4%
ord 4053
 
4.1%
den 3886
 
3.9%
clt 3110
 
3.1%
lax 2866
 
2.9%
phx 2460
 
2.5%
sea 2451
 
2.5%
las 2391
 
2.4%
iah 2180
 
2.2%
Other values (362) 67060
67.1%
2025-07-07T15:09:46.901064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 33825
 
11.3%
L 27711
 
9.2%
S 25102
 
8.4%
D 23886
 
8.0%
T 16671
 
5.6%
C 15373
 
5.1%
O 15190
 
5.1%
M 13305
 
4.4%
F 12605
 
4.2%
W 12152
 
4.1%
Other values (16) 104180
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 33825
 
11.3%
L 27711
 
9.2%
S 25102
 
8.4%
D 23886
 
8.0%
T 16671
 
5.6%
C 15373
 
5.1%
O 15190
 
5.1%
M 13305
 
4.4%
F 12605
 
4.2%
W 12152
 
4.1%
Other values (16) 104180
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 33825
 
11.3%
L 27711
 
9.2%
S 25102
 
8.4%
D 23886
 
8.0%
T 16671
 
5.6%
C 15373
 
5.1%
O 15190
 
5.1%
M 13305
 
4.4%
F 12605
 
4.2%
W 12152
 
4.1%
Other values (16) 104180
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 33825
 
11.3%
L 27711
 
9.2%
S 25102
 
8.4%
D 23886
 
8.0%
T 16671
 
5.6%
C 15373
 
5.1%
O 15190
 
5.1%
M 13305
 
4.4%
F 12605
 
4.2%
W 12152
 
4.1%
Other values (16) 104180
34.7%
Distinct366
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:47.276112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.12925
Min length8

Characters and Unicode

Total characters1312925
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowPunta Gorda, FL
2nd rowDallas/Fort Worth, TX
3rd rowNew Bern/Morehead/Beaufort, NC
4th rowAlbuquerque, NM
5th rowPittsburgh, PA
ValueCountFrequency (%)
tx 11010
 
4.7%
ca 10574
 
4.5%
fl 8561
 
3.7%
ga 5469
 
2.3%
il 5462
 
2.3%
chicago 5219
 
2.2%
atlanta 5099
 
2.2%
san 5043
 
2.2%
ny 4994
 
2.1%
new 4616
 
2.0%
Other values (442) 166755
71.6%
2025-07-07T15:09:47.833304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
132802
 
10.1%
a 100128
 
7.6%
, 100000
 
7.6%
o 73394
 
5.6%
e 69031
 
5.3%
t 65240
 
5.0%
n 64075
 
4.9%
l 58680
 
4.5%
i 50423
 
3.8%
r 47833
 
3.6%
Other values (48) 551319
42.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1312925
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
132802
 
10.1%
a 100128
 
7.6%
, 100000
 
7.6%
o 73394
 
5.6%
e 69031
 
5.3%
t 65240
 
5.0%
n 64075
 
4.9%
l 58680
 
4.5%
i 50423
 
3.8%
r 47833
 
3.6%
Other values (48) 551319
42.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1312925
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
132802
 
10.1%
a 100128
 
7.6%
, 100000
 
7.6%
o 73394
 
5.6%
e 69031
 
5.3%
t 65240
 
5.0%
n 64075
 
4.9%
l 58680
 
4.5%
i 50423
 
3.8%
r 47833
 
3.6%
Other values (48) 551319
42.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1312925
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
132802
 
10.1%
a 100128
 
7.6%
, 100000
 
7.6%
o 73394
 
5.6%
e 69031
 
5.3%
t 65240
 
5.0%
n 64075
 
4.9%
l 58680
 
4.5%
i 50423
 
3.8%
r 47833
 
3.6%
Other values (48) 551319
42.0%

DEST
Text

Distinct376
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:48.318089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300000
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowSPI
2nd rowLAX
3rd rowCLT
4th rowDEN
5th rowSTL
ValueCountFrequency (%)
atl 5095
 
5.1%
dfw 4239
 
4.2%
ord 4157
 
4.2%
den 3956
 
4.0%
clt 3269
 
3.3%
lax 2804
 
2.8%
phx 2628
 
2.6%
las 2413
 
2.4%
sea 2323
 
2.3%
mco 2171
 
2.2%
Other values (366) 66945
66.9%
2025-07-07T15:09:48.973182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 33522
 
11.2%
L 27841
 
9.3%
S 25056
 
8.4%
D 23828
 
7.9%
T 16592
 
5.5%
C 15450
 
5.1%
O 15428
 
5.1%
M 13505
 
4.5%
F 12402
 
4.1%
P 11838
 
3.9%
Other values (16) 104538
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 33522
 
11.2%
L 27841
 
9.3%
S 25056
 
8.4%
D 23828
 
7.9%
T 16592
 
5.5%
C 15450
 
5.1%
O 15428
 
5.1%
M 13505
 
4.5%
F 12402
 
4.1%
P 11838
 
3.9%
Other values (16) 104538
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 33522
 
11.2%
L 27841
 
9.3%
S 25056
 
8.4%
D 23828
 
7.9%
T 16592
 
5.5%
C 15450
 
5.1%
O 15428
 
5.1%
M 13505
 
4.5%
F 12402
 
4.1%
P 11838
 
3.9%
Other values (16) 104538
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 33522
 
11.2%
L 27841
 
9.3%
S 25056
 
8.4%
D 23828
 
7.9%
T 16592
 
5.5%
C 15450
 
5.1%
O 15428
 
5.1%
M 13505
 
4.5%
F 12402
 
4.1%
P 11838
 
3.9%
Other values (16) 104538
34.8%
Distinct369
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:49.398469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.09602
Min length8

Characters and Unicode

Total characters1309602
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowSpringfield, IL
2nd rowLos Angeles, CA
3rd rowCharlotte, NC
4th rowDenver, CO
5th rowSt. Louis, MO
ValueCountFrequency (%)
tx 10708
 
4.6%
ca 10524
 
4.5%
fl 8764
 
3.8%
il 5587
 
2.4%
ga 5466
 
2.4%
chicago 5359
 
2.3%
atlanta 5095
 
2.2%
san 5036
 
2.2%
ny 4825
 
2.1%
nc 4634
 
2.0%
Other values (445) 166412
71.6%
2025-07-07T15:09:50.025938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
132410
 
10.1%
a 100289
 
7.7%
, 100000
 
7.6%
o 73036
 
5.6%
e 68819
 
5.3%
t 64523
 
4.9%
n 63980
 
4.9%
l 58137
 
4.4%
i 50206
 
3.8%
r 47275
 
3.6%
Other values (48) 550927
42.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1309602
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
132410
 
10.1%
a 100289
 
7.7%
, 100000
 
7.6%
o 73036
 
5.6%
e 68819
 
5.3%
t 64523
 
4.9%
n 63980
 
4.9%
l 58137
 
4.4%
i 50206
 
3.8%
r 47275
 
3.6%
Other values (48) 550927
42.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1309602
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
132410
 
10.1%
a 100289
 
7.7%
, 100000
 
7.6%
o 73036
 
5.6%
e 68819
 
5.3%
t 64523
 
4.9%
n 63980
 
4.9%
l 58137
 
4.4%
i 50206
 
3.8%
r 47275
 
3.6%
Other values (48) 550927
42.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1309602
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
132410
 
10.1%
a 100289
 
7.7%
, 100000
 
7.6%
o 73036
 
5.6%
e 68819
 
5.3%
t 64523
 
4.9%
n 63980
 
4.9%
l 58137
 
4.4%
i 50206
 
3.8%
r 47275
 
3.6%
Other values (48) 550927
42.1%

CRS_DEP_TIME
Real number (ℝ)

High correlation 

Distinct1238
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1329.3123
Minimum4
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:50.253681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile605
Q1915
median1322
Q31730
95-th percentile2120
Maximum2359
Range2355
Interquartile range (IQR)815

Descriptive statistics

Standard deviation485.41853
Coefficient of variation (CV)0.36516515
Kurtosis-1.0347969
Mean1329.3123
Median Absolute Deviation (MAD)407
Skewness0.085529116
Sum1.3293123 × 108
Variance235631.15
MonotonicityNot monotonic
2025-07-07T15:09:50.461653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 2073
 
2.1%
700 1687
 
1.7%
800 1034
 
1.0%
830 700
 
0.7%
900 685
 
0.7%
630 663
 
0.7%
1000 646
 
0.6%
730 645
 
0.6%
1100 568
 
0.6%
715 529
 
0.5%
Other values (1228) 90770
90.8%
ValueCountFrequency (%)
4 1
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
14 3
 
< 0.1%
15 9
< 0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
2359 118
0.1%
2358 10
 
< 0.1%
2357 3
 
< 0.1%
2356 8
 
< 0.1%
2355 54
0.1%
2354 1
 
< 0.1%
2353 2
 
< 0.1%
2352 3
 
< 0.1%
2351 3
 
< 0.1%
2350 29
 
< 0.1%

DEP_TIME
Real number (ℝ)

High correlation  Missing 

Distinct1332
Distinct (%)1.4%
Missing2576
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean1331.0057
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:50.655638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile603
Q1918
median1325
Q31738
95-th percentile2133
Maximum2400
Range2399
Interquartile range (IQR)820

Descriptive statistics

Standard deviation498.86463
Coefficient of variation (CV)0.37480276
Kurtosis-0.96405827
Mean1331.0057
Median Absolute Deviation (MAD)411
Skewness0.040328846
Sum1.296719 × 108
Variance248865.91
MonotonicityNot monotonic
2025-07-07T15:09:50.864796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 279
 
0.3%
556 253
 
0.3%
655 227
 
0.2%
557 222
 
0.2%
558 220
 
0.2%
554 214
 
0.2%
559 214
 
0.2%
600 202
 
0.2%
656 198
 
0.2%
700 188
 
0.2%
Other values (1322) 95207
95.2%
(Missing) 2576
 
2.6%
ValueCountFrequency (%)
1 13
< 0.1%
2 8
< 0.1%
3 9
< 0.1%
4 14
< 0.1%
5 8
< 0.1%
6 11
< 0.1%
7 9
< 0.1%
8 11
< 0.1%
9 6
< 0.1%
10 10
< 0.1%
ValueCountFrequency (%)
2400 8
 
< 0.1%
2359 9
< 0.1%
2358 16
< 0.1%
2357 20
< 0.1%
2356 14
< 0.1%
2355 18
< 0.1%
2354 9
< 0.1%
2353 19
< 0.1%
2352 14
< 0.1%
2351 20
< 0.1%

DEP_DELAY
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct590
Distinct (%)0.6%
Missing2577
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean10.178007
Minimum-81
Maximum1512
Zeros4851
Zeros (%)4.9%
Negative59451
Negative (%)59.5%
Memory size781.4 KiB
2025-07-07T15:09:51.063935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-81
5-th percentile-10
Q1-6
median-2
Q36
95-th percentile72
Maximum1512
Range1593
Interquartile range (IQR)12

Descriptive statistics

Standard deviation49.398318
Coefficient of variation (CV)4.8534371
Kurtosis189.01187
Mean10.178007
Median Absolute Deviation (MAD)4
Skewness10.773897
Sum991572
Variance2440.1939
MonotonicityNot monotonic
2025-07-07T15:09:51.270469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 8093
 
8.1%
-4 7517
 
7.5%
-3 7078
 
7.1%
-6 6340
 
6.3%
-2 6272
 
6.3%
-1 5498
 
5.5%
-7 5044
 
5.0%
0 4851
 
4.9%
-8 3979
 
4.0%
-9 2885
 
2.9%
Other values (580) 39866
39.9%
ValueCountFrequency (%)
-81 1
 
< 0.1%
-42 2
 
< 0.1%
-38 1
 
< 0.1%
-36 1
 
< 0.1%
-35 2
 
< 0.1%
-34 2
 
< 0.1%
-32 1
 
< 0.1%
-29 5
< 0.1%
-28 1
 
< 0.1%
-27 8
< 0.1%
ValueCountFrequency (%)
1512 1
< 0.1%
1469 1
< 0.1%
1441 1
< 0.1%
1376 1
< 0.1%
1286 1
< 0.1%
1256 1
< 0.1%
1255 1
< 0.1%
1214 1
< 0.1%
1211 1
< 0.1%
1205 1
< 0.1%

TAXI_OUT
Real number (ℝ)

Missing 

Distinct135
Distinct (%)0.1%
Missing2618
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean16.679992
Minimum1
Maximum163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:51.470650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q111
median14
Q319
95-th percentile33
Maximum163
Range162
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.1706548
Coefficient of variation (CV)0.5497997
Kurtosis23.061235
Mean16.679992
Median Absolute Deviation (MAD)4
Skewness3.4144476
Sum1624331
Variance84.100909
MonotonicityNot monotonic
2025-07-07T15:09:51.684209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 8093
 
8.1%
13 7826
 
7.8%
11 7818
 
7.8%
14 7157
 
7.2%
10 7068
 
7.1%
15 6461
 
6.5%
16 5450
 
5.5%
9 5378
 
5.4%
17 4753
 
4.8%
18 4013
 
4.0%
Other values (125) 33365
33.4%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 3
 
< 0.1%
3 20
 
< 0.1%
4 83
 
0.1%
5 238
 
0.2%
6 780
 
0.8%
7 1803
 
1.8%
8 3500
3.5%
9 5378
5.4%
10 7068
7.1%
ValueCountFrequency (%)
163 1
< 0.1%
162 1
< 0.1%
158 1
< 0.1%
157 2
< 0.1%
151 1
< 0.1%
150 1
< 0.1%
147 1
< 0.1%
143 2
< 0.1%
142 2
< 0.1%
139 1
< 0.1%

WHEELS_OFF
Real number (ℝ)

High correlation  Missing 

Distinct1338
Distinct (%)1.4%
Missing2618
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean1353.6437
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:51.895924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile618
Q1932
median1338
Q31752
95-th percentile2146
Maximum2400
Range2399
Interquartile range (IQR)820

Descriptive statistics

Standard deviation500.4562
Coefficient of variation (CV)0.36971044
Kurtosis-0.90000081
Mean1353.6437
Median Absolute Deviation (MAD)410
Skewness0.0066238008
Sum1.3182053 × 108
Variance250456.41
MonotonicityNot monotonic
2025-07-07T15:09:52.098387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
611 189
 
0.2%
610 164
 
0.2%
608 163
 
0.2%
615 160
 
0.2%
712 149
 
0.1%
711 148
 
0.1%
715 148
 
0.1%
619 147
 
0.1%
613 146
 
0.1%
607 146
 
0.1%
Other values (1328) 95822
95.8%
(Missing) 2618
 
2.6%
ValueCountFrequency (%)
1 22
< 0.1%
2 19
< 0.1%
3 14
< 0.1%
4 12
< 0.1%
5 20
< 0.1%
6 11
< 0.1%
7 14
< 0.1%
8 18
< 0.1%
9 14
< 0.1%
10 23
< 0.1%
ValueCountFrequency (%)
2400 9
 
< 0.1%
2359 23
< 0.1%
2358 17
< 0.1%
2357 12
< 0.1%
2356 24
< 0.1%
2355 14
< 0.1%
2354 18
< 0.1%
2353 12
< 0.1%
2352 20
< 0.1%
2351 18
< 0.1%

WHEELS_ON
Real number (ℝ)

High correlation  Missing 

Distinct1421
Distinct (%)1.5%
Missing2655
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean1463.905
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:52.299311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile654
Q11050
median1502
Q31909
95-th percentile2247
Maximum2400
Range2399
Interquartile range (IQR)859

Descriptive statistics

Standard deviation527.43063
Coefficient of variation (CV)0.36029019
Kurtosis-0.43288968
Mean1463.905
Median Absolute Deviation (MAD)416
Skewness-0.31773495
Sum1.4250384 × 108
Variance278183.07
MonotonicityNot monotonic
2025-07-07T15:09:52.504036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1820 136
 
0.1%
930 132
 
0.1%
1651 131
 
0.1%
1802 124
 
0.1%
1836 123
 
0.1%
1654 123
 
0.1%
1641 121
 
0.1%
1715 121
 
0.1%
1627 120
 
0.1%
2113 120
 
0.1%
Other values (1411) 96094
96.1%
(Missing) 2655
 
2.7%
ValueCountFrequency (%)
1 48
< 0.1%
2 49
< 0.1%
3 44
< 0.1%
4 47
< 0.1%
5 49
< 0.1%
6 36
< 0.1%
7 45
< 0.1%
8 36
< 0.1%
9 31
< 0.1%
10 40
< 0.1%
ValueCountFrequency (%)
2400 45
< 0.1%
2359 45
< 0.1%
2358 55
0.1%
2357 62
0.1%
2356 48
< 0.1%
2355 52
0.1%
2354 60
0.1%
2353 63
0.1%
2352 42
< 0.1%
2351 39
< 0.1%

TAXI_IN
Real number (ℝ)

High correlation  Missing 

Distinct120
Distinct (%)0.1%
Missing2655
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean7.6952899
Minimum1
Maximum261
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:52.704609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q39
95-th percentile18
Maximum261
Range260
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.4810638
Coefficient of variation (CV)0.84221178
Kurtosis92.748798
Mean7.6952899
Median Absolute Deviation (MAD)2
Skewness6.088001
Sum749098
Variance42.004188
MonotonicityNot monotonic
2025-07-07T15:09:52.908902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 15144
15.1%
5 14174
14.2%
6 11318
11.3%
3 10444
10.4%
7 9078
9.1%
8 6843
6.8%
9 5283
 
5.3%
10 4148
 
4.1%
11 3304
 
3.3%
2 3062
 
3.1%
Other values (110) 14547
14.5%
(Missing) 2655
 
2.7%
ValueCountFrequency (%)
1 183
 
0.2%
2 3062
 
3.1%
3 10444
10.4%
4 15144
15.1%
5 14174
14.2%
6 11318
11.3%
7 9078
9.1%
8 6843
6.8%
9 5283
 
5.3%
10 4148
 
4.1%
ValueCountFrequency (%)
261 1
< 0.1%
187 1
< 0.1%
184 1
< 0.1%
177 1
< 0.1%
173 1
< 0.1%
160 1
< 0.1%
155 1
< 0.1%
153 1
< 0.1%
151 1
< 0.1%
145 1
< 0.1%

CRS_ARR_TIME
Real number (ℝ)

High correlation 

Distinct1327
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1491.4711
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:53.105197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile725
Q11108
median1519
Q31919
95-th percentile2254
Maximum2359
Range2358
Interquartile range (IQR)811

Descriptive statistics

Standard deviation512.56987
Coefficient of variation (CV)0.34366731
Kurtosis-0.45143412
Mean1491.4711
Median Absolute Deviation (MAD)406
Skewness-0.28739952
Sum1.4914711 × 108
Variance262727.88
MonotonicityNot monotonic
2025-07-07T15:09:53.300648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2359 324
 
0.3%
950 297
 
0.3%
1900 290
 
0.3%
1000 276
 
0.3%
2030 267
 
0.3%
1400 265
 
0.3%
2000 264
 
0.3%
2100 262
 
0.3%
1555 262
 
0.3%
1835 259
 
0.3%
Other values (1317) 97234
97.2%
ValueCountFrequency (%)
1 23
 
< 0.1%
2 27
 
< 0.1%
3 30
 
< 0.1%
4 22
 
< 0.1%
5 82
0.1%
6 28
 
< 0.1%
7 16
 
< 0.1%
8 23
 
< 0.1%
9 19
 
< 0.1%
10 89
0.1%
ValueCountFrequency (%)
2359 324
0.3%
2358 103
 
0.1%
2357 97
 
0.1%
2356 78
 
0.1%
2355 171
0.2%
2354 71
 
0.1%
2353 68
 
0.1%
2352 69
 
0.1%
2351 52
 
0.1%
2350 142
0.1%

ARR_TIME
Real number (ℝ)

High correlation  Missing 

Distinct1410
Distinct (%)1.4%
Missing2655
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean1467.1255
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:53.495749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile652
Q11053
median1506
Q31913
95-th percentile2249
Maximum2400
Range2399
Interquartile range (IQR)860

Descriptive statistics

Standard deviation532.51335
Coefficient of variation (CV)0.36296373
Kurtosis-0.35065461
Mean1467.1255
Median Absolute Deviation (MAD)413
Skewness-0.36097268
Sum1.4281733 × 108
Variance283570.47
MonotonicityNot monotonic
2025-07-07T15:09:53.701565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1115 130
 
0.1%
1343 129
 
0.1%
1020 123
 
0.1%
1655 123
 
0.1%
1840 122
 
0.1%
1230 122
 
0.1%
1052 122
 
0.1%
1547 121
 
0.1%
1550 121
 
0.1%
944 121
 
0.1%
Other values (1400) 96111
96.1%
(Missing) 2655
 
2.7%
ValueCountFrequency (%)
1 67
0.1%
2 50
0.1%
3 61
0.1%
4 42
< 0.1%
5 62
0.1%
6 40
< 0.1%
7 54
0.1%
8 39
< 0.1%
9 48
< 0.1%
10 60
0.1%
ValueCountFrequency (%)
2400 48
< 0.1%
2359 43
< 0.1%
2358 71
0.1%
2357 59
0.1%
2356 56
0.1%
2355 53
0.1%
2354 59
0.1%
2353 62
0.1%
2352 63
0.1%
2351 68
0.1%

ARR_DELAY
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct613
Distinct (%)0.6%
Missing2852
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean4.3537592
Minimum-88
Maximum1520
Zeros1853
Zeros (%)1.9%
Negative62508
Negative (%)62.5%
Memory size781.4 KiB
2025-07-07T15:09:53.898021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-88
5-th percentile-27
Q1-15
median-7
Q37
95-th percentile70
Maximum1520
Range1608
Interquartile range (IQR)22

Descriptive statistics

Standard deviation51.360244
Coefficient of variation (CV)11.796758
Kurtosis163.6387
Mean4.3537592
Median Absolute Deviation (MAD)10
Skewness9.7045591
Sum422959
Variance2637.8747
MonotonicityNot monotonic
2025-07-07T15:09:54.110343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-11 2861
 
2.9%
-12 2845
 
2.8%
-13 2834
 
2.8%
-9 2825
 
2.8%
-10 2787
 
2.8%
-7 2753
 
2.8%
-14 2747
 
2.7%
-8 2690
 
2.7%
-15 2631
 
2.6%
-16 2534
 
2.5%
Other values (603) 69641
69.6%
(Missing) 2852
 
2.9%
ValueCountFrequency (%)
-88 1
 
< 0.1%
-68 1
 
< 0.1%
-67 1
 
< 0.1%
-66 2
 
< 0.1%
-63 1
 
< 0.1%
-62 2
 
< 0.1%
-61 4
< 0.1%
-60 3
 
< 0.1%
-59 9
< 0.1%
-58 1
 
< 0.1%
ValueCountFrequency (%)
1520 1
< 0.1%
1473 1
< 0.1%
1458 1
< 0.1%
1371 1
< 0.1%
1285 1
< 0.1%
1270 1
< 0.1%
1247 1
< 0.1%
1207 1
< 0.1%
1200 1
< 0.1%
1199 1
< 0.1%

CANCELLED
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
0.0
97373 
1.0
 
2627

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 97373
97.4%
1.0 2627
 
2.6%

Length

2025-07-07T15:09:54.305733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T15:09:54.440339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 97373
97.4%
1.0 2627
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 197373
65.8%
. 100000
33.3%
1 2627
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 197373
65.8%
. 100000
33.3%
1 2627
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 197373
65.8%
. 100000
33.3%
1 2627
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 197373
65.8%
. 100000
33.3%
1 2627
 
0.9%

CANCELLATION_CODE
Categorical

High correlation  Missing 

Distinct4
Distinct (%)0.2%
Missing97373
Missing (%)97.4%
Memory size781.4 KiB
B
930 
D
827 
A
671 
C
199 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2627
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowC
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
B 930
 
0.9%
D 827
 
0.8%
A 671
 
0.7%
C 199
 
0.2%
(Missing) 97373
97.4%

Length

2025-07-07T15:09:54.582626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T15:09:54.724776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 930
35.4%
d 827
31.5%
a 671
25.5%
c 199
 
7.6%

Most occurring characters

ValueCountFrequency (%)
B 930
35.4%
D 827
31.5%
A 671
25.5%
C 199
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2627
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 930
35.4%
D 827
31.5%
A 671
25.5%
C 199
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2627
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 930
35.4%
D 827
31.5%
A 671
25.5%
C 199
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2627
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 930
35.4%
D 827
31.5%
A 671
25.5%
C 199
 
7.6%

DIVERTED
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
0.0
99775 
1.0
 
225

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 99775
99.8%
1.0 225
 
0.2%

Length

2025-07-07T15:09:54.881108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T15:09:55.008296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 99775
99.8%
1.0 225
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 199775
66.6%
. 100000
33.3%
1 225
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 199775
66.6%
. 100000
33.3%
1 225
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 199775
66.6%
. 100000
33.3%
1 225
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 199775
66.6%
. 100000
33.3%
1 225
 
0.1%

CRS_ELAPSED_TIME
Real number (ℝ)

High correlation 

Distinct502
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.73547
Minimum20
Maximum695
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:55.166789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile64
Q190
median125
Q3173
95-th percentile300
Maximum695
Range675
Interquartile range (IQR)83

Descriptive statistics

Standard deviation71.852142
Coefficient of variation (CV)0.50339374
Kurtosis2.5963346
Mean142.73547
Median Absolute Deviation (MAD)40
Skewness1.4467089
Sum14273547
Variance5162.7302
MonotonicityNot monotonic
2025-07-07T15:09:55.375825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 1924
 
1.9%
85 1812
 
1.8%
80 1729
 
1.7%
70 1557
 
1.6%
75 1500
 
1.5%
95 1463
 
1.5%
115 1273
 
1.3%
105 1258
 
1.3%
120 1241
 
1.2%
110 1233
 
1.2%
Other values (492) 85010
85.0%
ValueCountFrequency (%)
20 3
< 0.1%
21 2
 
< 0.1%
23 1
 
< 0.1%
24 1
 
< 0.1%
25 3
< 0.1%
26 3
< 0.1%
29 1
 
< 0.1%
30 2
 
< 0.1%
32 5
< 0.1%
33 4
< 0.1%
ValueCountFrequency (%)
695 1
< 0.1%
684 1
< 0.1%
680 1
< 0.1%
675 2
< 0.1%
670 2
< 0.1%
665 1
< 0.1%
660 2
< 0.1%
652 1
< 0.1%
650 2
< 0.1%
646 1
< 0.1%

ELAPSED_TIME
Real number (ℝ)

High correlation  Missing 

Distinct514
Distinct (%)0.5%
Missing2852
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean137.07864
Minimum17
Maximum706
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:55.602775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile56
Q185
median120
Q3168
95-th percentile293
Maximum706
Range689
Interquartile range (IQR)83

Descriptive statistics

Standard deviation72.041746
Coefficient of variation (CV)0.52555048
Kurtosis2.5343943
Mean137.07864
Median Absolute Deviation (MAD)40
Skewness1.4217463
Sum13316916
Variance5190.0132
MonotonicityNot monotonic
2025-07-07T15:09:55.807698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87 798
 
0.8%
79 780
 
0.8%
85 778
 
0.8%
83 773
 
0.8%
76 764
 
0.8%
82 760
 
0.8%
81 757
 
0.8%
75 755
 
0.8%
72 750
 
0.8%
77 747
 
0.7%
Other values (504) 89486
89.5%
(Missing) 2852
 
2.9%
ValueCountFrequency (%)
17 1
 
< 0.1%
18 1
 
< 0.1%
19 1
 
< 0.1%
21 1
 
< 0.1%
22 2
 
< 0.1%
23 1
 
< 0.1%
24 2
 
< 0.1%
25 5
< 0.1%
26 3
 
< 0.1%
28 9
< 0.1%
ValueCountFrequency (%)
706 1
< 0.1%
696 1
< 0.1%
687 1
< 0.1%
682 1
< 0.1%
676 1
< 0.1%
668 1
< 0.1%
657 1
< 0.1%
648 1
< 0.1%
644 1
< 0.1%
641 1
< 0.1%

AIR_TIME
Real number (ℝ)

High correlation  Missing 

Distinct492
Distinct (%)0.5%
Missing2852
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean112.71605
Minimum9
Maximum669
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:56.008991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile35
Q162
median95
Q3142
95-th percentile267
Maximum669
Range660
Interquartile range (IQR)80

Descriptive statistics

Standard deviation70.10449
Coefficient of variation (CV)0.62195658
Kurtosis2.6192588
Mean112.71605
Median Absolute Deviation (MAD)38
Skewness1.4556215
Sum10950139
Variance4914.6396
MonotonicityNot monotonic
2025-07-07T15:09:56.212559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 860
 
0.9%
43 858
 
0.9%
63 851
 
0.9%
65 835
 
0.8%
60 832
 
0.8%
58 828
 
0.8%
55 827
 
0.8%
59 824
 
0.8%
53 818
 
0.8%
66 811
 
0.8%
Other values (482) 88804
88.8%
(Missing) 2852
 
2.9%
ValueCountFrequency (%)
9 4
 
< 0.1%
10 4
 
< 0.1%
11 2
 
< 0.1%
12 2
 
< 0.1%
13 4
 
< 0.1%
14 3
 
< 0.1%
15 6
 
< 0.1%
16 15
 
< 0.1%
17 29
< 0.1%
18 49
< 0.1%
ValueCountFrequency (%)
669 1
< 0.1%
659 1
< 0.1%
657 1
< 0.1%
654 2
< 0.1%
634 1
< 0.1%
623 1
< 0.1%
621 1
< 0.1%
614 1
< 0.1%
611 1
< 0.1%
606 1
< 0.1%

DISTANCE
Real number (ℝ)

High correlation 

Distinct1611
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean812.94666
Minimum29
Maximum5812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:56.416890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile175
Q1386
median657
Q31048
95-th percentile2153
Maximum5812
Range5783
Interquartile range (IQR)662

Descriptive statistics

Standard deviation590.42474
Coefficient of variation (CV)0.72627734
Kurtosis2.9035067
Mean812.94666
Median Absolute Deviation (MAD)320
Skewness1.5102644
Sum81294666
Variance348601.37
MonotonicityNot monotonic
2025-07-07T15:09:56.617627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337 635
 
0.6%
399 503
 
0.5%
296 495
 
0.5%
594 416
 
0.4%
224 410
 
0.4%
404 397
 
0.4%
214 393
 
0.4%
733 364
 
0.4%
862 363
 
0.4%
588 355
 
0.4%
Other values (1601) 95669
95.7%
ValueCountFrequency (%)
29 1
 
< 0.1%
30 1
 
< 0.1%
31 12
 
< 0.1%
41 2
 
< 0.1%
45 11
 
< 0.1%
50 3
 
< 0.1%
54 2
 
< 0.1%
61 4
 
< 0.1%
66 6
 
< 0.1%
67 61
0.1%
ValueCountFrequency (%)
5812 1
 
< 0.1%
5095 5
 
< 0.1%
4983 13
< 0.1%
4962 3
 
< 0.1%
4904 2
 
< 0.1%
4817 4
 
< 0.1%
4757 1
 
< 0.1%
4678 2
 
< 0.1%
4502 12
< 0.1%
4475 1
 
< 0.1%

DELAY_DUE_CARRIER
Real number (ℝ)

High correlation  Zeros 

Distinct422
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.48232
Minimum0
Maximum1473
Zeros90096
Zeros (%)90.1%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:56.820009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile20
Maximum1473
Range1473
Interquartile range (IQR)0

Descriptive statistics

Standard deviation31.972682
Coefficient of variation (CV)7.1330655
Kurtosis539.85777
Mean4.48232
Median Absolute Deviation (MAD)0
Skewness19.571832
Sum448232
Variance1022.2524
MonotonicityNot monotonic
2025-07-07T15:09:57.025211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 90096
90.1%
2 312
 
0.3%
6 300
 
0.3%
3 297
 
0.3%
15 293
 
0.3%
5 285
 
0.3%
1 284
 
0.3%
16 276
 
0.3%
4 257
 
0.3%
9 256
 
0.3%
Other values (412) 7344
 
7.3%
ValueCountFrequency (%)
0 90096
90.1%
1 284
 
0.3%
2 312
 
0.3%
3 297
 
0.3%
4 257
 
0.3%
5 285
 
0.3%
6 300
 
0.3%
7 249
 
0.2%
8 240
 
0.2%
9 256
 
0.3%
ValueCountFrequency (%)
1473 1
< 0.1%
1458 1
< 0.1%
1376 1
< 0.1%
1256 1
< 0.1%
1155 1
< 0.1%
1147 1
< 0.1%
1121 1
< 0.1%
1115 1
< 0.1%
1088 1
< 0.1%
1066 1
< 0.1%

DELAY_DUE_WEATHER
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct224
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69679
Minimum0
Maximum1285
Zeros98965
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:57.231331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1285
Range1285
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.81459
Coefficient of variation (CV)18.390892
Kurtosis2968.2942
Mean0.69679
Median Absolute Deviation (MAD)0
Skewness44.380019
Sum69679
Variance164.21372
MonotonicityNot monotonic
2025-07-07T15:09:57.443458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 98965
99.0%
2 24
 
< 0.1%
16 22
 
< 0.1%
7 22
 
< 0.1%
6 21
 
< 0.1%
8 20
 
< 0.1%
32 19
 
< 0.1%
9 19
 
< 0.1%
3 19
 
< 0.1%
18 18
 
< 0.1%
Other values (214) 851
 
0.9%
ValueCountFrequency (%)
0 98965
99.0%
1 17
 
< 0.1%
2 24
 
< 0.1%
3 19
 
< 0.1%
4 16
 
< 0.1%
5 17
 
< 0.1%
6 21
 
< 0.1%
7 22
 
< 0.1%
8 20
 
< 0.1%
9 19
 
< 0.1%
ValueCountFrequency (%)
1285 1
< 0.1%
1059 1
< 0.1%
1000 1
< 0.1%
989 1
< 0.1%
825 1
< 0.1%
780 1
< 0.1%
733 1
< 0.1%
586 1
< 0.1%
565 1
< 0.1%
554 1
< 0.1%

DELAY_DUE_NAS
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct252
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.39749
Minimum0
Maximum1207
Zeros91256
Zeros (%)91.3%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:57.648279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile15
Maximum1207
Range1207
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.190141
Coefficient of variation (CV)6.3358516
Kurtosis1217.054
Mean2.39749
Median Absolute Deviation (MAD)0
Skewness24.492451
Sum239749
Variance230.74038
MonotonicityNot monotonic
2025-07-07T15:09:57.861849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 91256
91.3%
1 449
 
0.4%
15 345
 
0.3%
3 319
 
0.3%
17 311
 
0.3%
16 305
 
0.3%
2 303
 
0.3%
5 287
 
0.3%
6 287
 
0.3%
18 279
 
0.3%
Other values (242) 5859
 
5.9%
ValueCountFrequency (%)
0 91256
91.3%
1 449
 
0.4%
2 303
 
0.3%
3 319
 
0.3%
4 278
 
0.3%
5 287
 
0.3%
6 287
 
0.3%
7 260
 
0.3%
8 236
 
0.2%
9 203
 
0.2%
ValueCountFrequency (%)
1207 1
< 0.1%
998 1
< 0.1%
978 1
< 0.1%
880 1
< 0.1%
865 1
< 0.1%
818 1
< 0.1%
696 1
< 0.1%
539 1
< 0.1%
442 1
< 0.1%
439 1
< 0.1%

DELAY_DUE_SECURITY
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02508
Minimum0
Maximum249
Zeros99898
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:58.061912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum249
Range249
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4182211
Coefficient of variation (CV)56.547891
Kurtosis18118.273
Mean0.02508
Median Absolute Deviation (MAD)0
Skewness119.24109
Sum2508
Variance2.0113511
MonotonicityNot monotonic
2025-07-07T15:09:58.262444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 99898
99.9%
1 7
 
< 0.1%
10 7
 
< 0.1%
17 6
 
< 0.1%
11 5
 
< 0.1%
15 5
 
< 0.1%
14 5
 
< 0.1%
18 4
 
< 0.1%
5 4
 
< 0.1%
27 4
 
< 0.1%
Other values (35) 55
 
0.1%
ValueCountFrequency (%)
0 99898
99.9%
1 7
 
< 0.1%
2 2
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%
5 4
 
< 0.1%
6 3
 
< 0.1%
7 4
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
249 1
< 0.1%
233 1
< 0.1%
117 1
< 0.1%
116 1
< 0.1%
91 1
< 0.1%
81 1
< 0.1%
59 1
< 0.1%
58 1
< 0.1%
54 1
< 0.1%
50 1
< 0.1%

DELAY_DUE_LATE_AIRCRAFT
Real number (ℝ)

High correlation  Zeros 

Distinct352
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.46947
Minimum0
Maximum1018
Zeros91377
Zeros (%)91.4%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-07-07T15:09:58.461448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile25
Maximum1018
Range1018
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24.553319
Coefficient of variation (CV)5.493564
Kurtosis330.71888
Mean4.46947
Median Absolute Deviation (MAD)0
Skewness13.548919
Sum446947
Variance602.8655
MonotonicityNot monotonic
2025-07-07T15:09:58.662988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 91377
91.4%
15 230
 
0.2%
16 200
 
0.2%
17 185
 
0.2%
18 184
 
0.2%
19 170
 
0.2%
12 166
 
0.2%
24 161
 
0.2%
20 156
 
0.2%
13 154
 
0.2%
Other values (342) 7017
 
7.0%
ValueCountFrequency (%)
0 91377
91.4%
1 129
 
0.1%
2 121
 
0.1%
3 126
 
0.1%
4 117
 
0.1%
5 132
 
0.1%
6 109
 
0.1%
7 134
 
0.1%
8 116
 
0.1%
9 127
 
0.1%
ValueCountFrequency (%)
1018 1
< 0.1%
1013 1
< 0.1%
1012 1
< 0.1%
960 1
< 0.1%
942 1
< 0.1%
889 1
< 0.1%
888 1
< 0.1%
840 1
< 0.1%
820 1
< 0.1%
802 1
< 0.1%

Interactions

2025-07-07T15:09:38.093786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:32.908411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:36.229783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:39.246635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:42.510505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:45.603661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:49.028926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:52.296196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:55.719011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:58.817951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:02.223509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:05.276098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:08.396019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:11.652996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:15.491640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:18.710975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:21.912985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:25.054418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:28.827589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:31.924714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:35.026716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:38.233984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:33.104304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:36.365242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:39.383047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:42.655005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:45.982599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:49.182911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:52.442008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:55.862986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:58.965705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:02.367973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:05.426830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:08.548264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:11.804434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:15.641587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:18.862716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:22.058385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:25.202333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:28.972654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:32.066092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:35.168033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:38.371285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:33.246186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:36.497267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:39.516634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:42.795041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:46.124162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:49.330398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:52.870647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:56.002433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:59.106189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:02.501896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:05.584211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:08.696172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:11.947262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:15.786105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:19.005491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:22.199688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:25.349370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:29.109924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:32.202542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:35.307768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:38.506474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:33.387223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:36.632635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:39.651559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:42.933058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:46.268046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:49.477122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:53.005836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:56.139808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:59.242544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:02.636661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:05.721923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:08.839748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:12.097587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:15.930308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:19.147766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:22.340041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:25.516448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:29.246542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:32.340721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:35.465090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:38.648575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:33.534473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:36.775772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:39.786347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:43.070535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:46.411541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:49.628234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:53.148239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:56.287785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:59.382384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:02.778051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:05.869821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:08.990442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:12.251143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:16.083440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:19.294174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:22.487788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:25.664084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:29.389401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:32.483076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:35.613047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:38.798600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:33.689363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:36.928100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:40.128144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:43.225894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:46.567833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:49.788172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:53.299359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:56.440069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:59.532041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:02.928378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:06.024905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:09.146891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:12.416057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:16.243002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:19.451760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:22.644680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:26.362719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:29.539444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:32.632625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:35.766999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2025-07-07T15:09:04.411172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2025-07-07T15:09:10.713544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:14.535480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:17.779830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:20.984926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:24.159409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:27.913484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:31.043938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:34.127590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:37.226690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:40.451484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:35.320703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:38.524514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:41.790415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:44.847078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:48.262898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:51.515266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:54.956327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:58.079518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:01.131940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:04.559245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:07.648520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:10.868855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:14.689299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:17.934901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:21.136493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:24.308513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:28.067263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:31.191946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:34.282081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:37.374368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:40.604559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:35.643634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:38.677901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:41.939469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:44.995977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:48.419297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:51.678758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:55.108299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:58.233021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:01.287547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:04.711330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:07.807598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:11.029731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:14.846844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:18.097013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:21.302182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:24.465836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:28.221201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:31.341429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:34.437610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:37.525626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:40.751689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:35.796316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:38.822033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:42.080102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:45.142659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:48.574572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:51.832665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:55.259056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:58.380722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:01.433050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:04.850850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:07.960421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:11.187524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:15.001279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:18.251032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:21.457856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:24.617431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:28.376320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:31.489828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:34.585297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:37.671496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:40.896687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:35.942887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:38.967973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:42.228237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:45.293067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:48.728023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:51.989374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:55.415108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:58.530123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:01.935470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:04.992287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:08.107198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:11.340568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:15.156806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:18.404978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:21.612250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:24.766248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:28.529505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:31.638366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:34.737139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:37.815447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:41.038009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:36.085167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:39.105021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:42.366289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:45.450032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:48.879182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:52.142965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:55.576305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:08:58.673151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:02.076081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:05.128001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:08.251733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:11.492000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:15.315169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:18.555723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:21.760320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:24.911342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:28.679933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:31.781638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:34.882691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-07-07T15:09:37.952790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-07-07T15:09:58.836059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AIRLINEAIRLINE_CODEAIRLINE_DOTAIR_TIMEARR_DELAYARR_TIMECANCELLATION_CODECANCELLEDCRS_ARR_TIMECRS_DEP_TIMECRS_ELAPSED_TIMEDELAY_DUE_CARRIERDELAY_DUE_LATE_AIRCRAFTDELAY_DUE_NASDELAY_DUE_SECURITYDELAY_DUE_WEATHERDEP_DELAYDEP_TIMEDISTANCEDIVERTEDDOT_CODEELAPSED_TIMEFL_NUMBERTAXI_INTAXI_OUTWHEELS_OFFWHEELS_ON
AIRLINE1.0001.0001.0000.1690.0260.0480.2490.0430.0500.0480.1730.0130.0160.0260.0070.0120.0260.0480.1760.0111.0000.1690.4150.0200.0910.0490.048
AIRLINE_CODE1.0001.0001.0000.1690.0260.0480.2490.0430.0500.0480.1730.0130.0160.0260.0070.0120.0260.0480.1760.0111.0000.1690.4150.0200.0910.0490.048
AIRLINE_DOT1.0001.0001.0000.1690.0260.0480.2490.0430.0500.0480.1730.0130.0160.0260.0070.0120.0260.0480.1760.0111.0000.1690.4150.0200.0910.0490.048
AIR_TIME0.1690.1690.1691.0000.0340.0440.0001.0000.052-0.0270.9840.0430.0050.0870.0100.0020.075-0.0280.9861.000-0.0640.979-0.3340.1250.071-0.0340.046
ARR_DELAY0.0260.0260.0260.0341.0000.1100.0001.0000.1120.130-0.0270.4800.4520.4400.0450.1570.6550.1660.0041.000-0.0330.099-0.0390.1190.2690.1710.117
ARR_TIME0.0480.0480.0480.0440.1101.0000.0001.0000.8960.7300.0390.0610.1150.066-0.0020.0250.1320.7530.0490.036-0.0010.0440.001-0.0280.0280.7680.976
CANCELLATION_CODE0.2490.2490.2490.0000.0000.0001.0001.0000.0700.0780.0611.0001.0001.0001.0001.0000.1450.0970.0661.0000.1540.0000.1330.0000.0000.0000.000
CANCELLED0.0430.0430.0431.0001.0001.0001.0001.0000.0160.0170.0110.0080.0150.0000.0000.0000.0440.0160.0130.0060.0321.0000.0081.0000.0000.0001.000
CRS_ARR_TIME0.0500.0500.0500.0520.1120.8960.0700.0161.0000.7940.0480.0760.1490.0660.0010.0290.1450.7920.0590.0170.0030.049-0.006-0.0370.0250.8000.904
CRS_DEP_TIME0.0480.0480.048-0.0270.1300.7300.0780.0170.7941.000-0.0340.0950.1730.0610.0030.0300.1600.969-0.0230.0120.010-0.0310.001-0.0660.0040.9530.751
CRS_ELAPSED_TIME0.1730.1730.1730.984-0.0270.0390.0610.0110.048-0.0341.0000.033-0.0030.0560.0080.0000.071-0.0340.9790.012-0.0220.974-0.3090.1630.112-0.0390.040
DELAY_DUE_CARRIER0.0130.0130.0130.0430.4800.0611.0000.0080.0760.0950.0331.0000.3980.3080.002-0.0180.4630.1270.0390.000-0.0180.058-0.0330.0130.0620.1260.066
DELAY_DUE_LATE_AIRCRAFT0.0160.0160.0160.0050.4520.1151.0000.0150.1490.173-0.0030.3981.0000.2930.0350.1100.4540.2070.0040.000-0.0200.015-0.0260.0180.0310.2020.123
DELAY_DUE_NAS0.0260.0260.0260.0870.4400.0661.0000.0000.0660.0610.0560.3080.2931.0000.0420.1360.2880.0830.0550.0000.0490.163-0.0390.1390.2420.0910.070
DELAY_DUE_SECURITY0.0070.0070.0070.0100.045-0.0021.0000.0000.0010.0030.0080.0020.0350.0421.0000.0030.0430.0060.0110.0000.0080.011-0.014-0.0000.0100.005-0.001
DELAY_DUE_WEATHER0.0120.0120.0120.0020.1570.0251.0000.0000.0290.0300.000-0.0180.1100.1360.0031.0000.1520.043-0.0020.0000.0250.0200.0110.0130.0600.0450.029
DEP_DELAY0.0260.0260.0260.0750.6550.1320.1450.0440.1450.1600.0710.4630.4540.2880.0430.1521.0000.2030.0840.018-0.1740.074-0.084-0.0510.0200.1990.141
DEP_TIME0.0480.0480.048-0.0280.1660.7530.0970.0160.7920.969-0.0340.1270.2070.0830.0060.0430.2031.000-0.0240.0130.004-0.0300.004-0.0640.0080.9840.774
DISTANCE0.1760.1760.1760.9860.0040.0490.0660.0130.059-0.0230.9790.0390.0040.0550.011-0.0020.084-0.0241.0000.009-0.0860.961-0.3510.1100.055-0.0300.052
DIVERTED0.0110.0110.0111.0001.0000.0361.0000.0060.0170.0120.0120.0000.0000.0000.0000.0000.0180.0130.0091.0000.0131.0000.0080.0530.0140.0130.039
DOT_CODE1.0001.0001.000-0.064-0.033-0.0010.1540.0320.0030.010-0.022-0.018-0.0200.0490.0080.025-0.1740.004-0.0860.0131.000-0.0060.3340.2130.2790.008-0.003
ELAPSED_TIME0.1690.1690.1690.9790.0990.0440.0001.0000.049-0.0310.9740.0580.0150.1630.0110.0200.074-0.0300.9611.000-0.0061.000-0.3050.2090.203-0.0320.046
FL_NUMBER0.4150.4150.415-0.334-0.0390.0010.1330.008-0.0060.001-0.309-0.033-0.026-0.039-0.0140.011-0.0840.004-0.3510.0080.334-0.3051.000-0.0280.0930.011-0.002
TAXI_IN0.0200.0200.0200.1250.119-0.0280.0001.000-0.037-0.0660.1630.0130.0180.139-0.0000.013-0.051-0.0640.1100.0530.2130.209-0.0281.0000.067-0.063-0.036
TAXI_OUT0.0910.0910.0910.0710.2690.0280.0000.0000.0250.0040.1120.0620.0310.2420.0100.0600.0200.0080.0550.0140.2790.2030.0930.0671.0000.0280.031
WHEELS_OFF0.0490.0490.049-0.0340.1710.7680.0000.0000.8000.953-0.0390.1260.2020.0910.0050.0450.1990.984-0.0300.0130.008-0.0320.011-0.0630.0281.0000.789
WHEELS_ON0.0480.0480.0480.0460.1170.9760.0001.0000.9040.7510.0400.0660.1230.070-0.0010.0290.1410.7740.0520.039-0.0030.046-0.002-0.0360.0310.7891.000

Missing values

2025-07-07T15:09:41.340409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-07T15:09:42.180514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-07T15:09:43.724994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

FL_DATEAIRLINEAIRLINE_DOTAIRLINE_CODEDOT_CODEFL_NUMBERORIGINORIGIN_CITYDESTDEST_CITYCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTWHEELS_OFFWHEELS_ONTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDCRS_ELAPSED_TIMEELAPSED_TIMEAIR_TIMEDISTANCEDELAY_DUE_CARRIERDELAY_DUE_WEATHERDELAY_DUE_NASDELAY_DUE_SECURITYDELAY_DUE_LATE_AIRCRAFT
02019-03-01Allegiant AirAllegiant Air: G4G4203681668PGDPunta Gorda, FLSPISpringfield, IL630620.0-10.09.0629.0731.07.0810738.0-32.00.0NaN0.0160.0138.0122.0994.00.00.00.00.00.0
12021-02-16American Airlines Inc.American Airlines Inc.: AAAA198052437DFWDallas/Fort Worth, TXLAXLos Angeles, CA1329NaNNaNNaNNaNNaNNaN1500NaNNaN1.0B0.0211.0NaNNaN1235.00.00.00.00.00.0
22022-04-12PSA Airlines Inc.PSA Airlines Inc.: OHOH203975560EWNNew Bern/Morehead/Beaufort, NCCLTCharlotte, NC625618.0-7.016.0634.0725.011.0744736.0-8.00.0NaN0.079.078.051.0221.00.00.00.00.00.0
32021-10-13Southwest Airlines Co.Southwest Airlines Co.: WNWN193931944ABQAlbuquerque, NMDENDenver, CO17151740.025.015.01755.01844.07.018351851.016.00.0NaN0.080.071.049.0349.010.00.00.00.06.0
42022-06-05Southwest Airlines Co.Southwest Airlines Co.: WNWN193933081PITPittsburgh, PASTLSt. Louis, MO535535.00.012.0547.0609.06.0620615.0-5.00.0NaN0.0105.0100.082.0554.00.00.00.00.00.0
52019-10-06Delta Air Lines Inc.Delta Air Lines Inc.: DLDL19790674LAXLos Angeles, CASEASeattle, WA11401138.0-2.014.01152.01405.011.014381416.0-22.00.0NaN0.0178.0158.0133.0954.00.00.00.00.00.0
62020-03-17Southwest Airlines Co.Southwest Airlines Co.: WNWN19393443OAKOakland, CAHOUHouston, TX20002019.019.013.02032.0143.07.0135150.015.00.0NaN0.0215.0211.0191.01642.015.00.00.00.00.0
72020-02-06Southwest Airlines Co.Southwest Airlines Co.: WNWN193931779SFOSan Francisco, CASANSan Diego, CA13401338.0-2.013.01351.01456.03.015201459.0-21.00.0NaN0.0100.081.065.0447.00.00.00.00.00.0
82019-03-11Delta Air Lines Inc.Delta Air Lines Inc.: DLDL197902135DTWDetroit, MILASLas Vegas, NV714710.0-4.030.0740.0854.010.0847904.017.00.0NaN0.0273.0294.0254.01749.00.00.017.00.00.0
92022-07-24Delta Air Lines Inc.Delta Air Lines Inc.: DLDL197901612DENDenver, COSLCSalt Lake City, UT20102038.028.016.02054.02159.05.021482204.016.00.0NaN0.098.086.065.0391.016.00.00.00.00.0
FL_DATEAIRLINEAIRLINE_DOTAIRLINE_CODEDOT_CODEFL_NUMBERORIGINORIGIN_CITYDESTDEST_CITYCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTWHEELS_OFFWHEELS_ONTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDCRS_ELAPSED_TIMEELAPSED_TIMEAIR_TIMEDISTANCEDELAY_DUE_CARRIERDELAY_DUE_WEATHERDELAY_DUE_NASDELAY_DUE_SECURITYDELAY_DUE_LATE_AIRCRAFT
999902019-02-19Southwest Airlines Co.Southwest Airlines Co.: WNWN19393377DENDenver, COSEASeattle, WA855857.02.014.0911.01043.04.011001047.0-13.00.0NaN0.0185.0170.0152.01024.00.00.00.00.00.0
999912019-02-21Southwest Airlines Co.Southwest Airlines Co.: WNWN193931752BWIBaltimore, MDTPATampa, FL825825.00.030.0855.01102.06.010501108.018.00.0NaN0.0145.0163.0127.0842.00.00.018.00.00.0
999922022-06-29Delta Air Lines Inc.Delta Air Lines Inc.: DLDL197901643ATLAtlanta, GAMSNMadison, WI950946.0-4.09.0955.01036.08.010481044.0-4.00.0NaN0.0118.0118.0101.0707.00.00.00.00.00.0
999932023-01-06Spirit Air LinesSpirit Air Lines: NKNK204161042SJUSan Juan, PRMCOOrlando, FL21302225.055.014.02239.017.039.0235056.066.00.0NaN0.0200.0211.0158.01189.00.00.011.00.055.0
999942023-08-05United Air Lines Inc.United Air Lines Inc.: UAUA199771478LASLas Vegas, NVIAHHouston, TX10231016.0-7.030.01046.01517.03.015331520.0-13.00.0NaN0.0190.0184.0151.01222.00.00.00.00.00.0
999952019-02-26Delta Air Lines Inc.Delta Air Lines Inc.: DLDL197902229CHSCharleston, SCATLAtlanta, GA12441241.0-3.013.01254.01344.010.014081354.0-14.00.0NaN0.084.073.050.0259.00.00.00.00.00.0
999962023-02-10Southwest Airlines Co.Southwest Airlines Co.: WNWN193932977MDWChicago, ILLAXLos Angeles, CA9251134.0129.019.01153.01322.011.012051333.088.00.0NaN0.0280.0239.0209.01750.088.00.00.00.00.0
999972019-06-26Endeavor Air Inc.Endeavor Air Inc.: 9E9E203635003ORFNorfolk, VAJFKNew York, NY17121715.03.025.01740.01831.06.018451837.0-8.00.0NaN0.093.082.051.0290.00.00.00.00.00.0
999982023-06-22Endeavor Air Inc.Endeavor Air Inc.: 9E9E203635173CVGCincinnati, OHLGANew York, NY14331436.03.014.01450.01617.09.016401626.0-14.00.0NaN0.0127.0110.087.0585.00.00.00.00.00.0
999992022-01-07Endeavor Air Inc.Endeavor Air Inc.: 9E9E203635227ATLAtlanta, GASHVShreveport, LA13451420.035.010.01430.01456.04.014381500.022.00.0NaN0.0113.0100.086.0551.00.00.00.00.022.0